Abstract
The cell formation problem is a crucial component of a cellular manufacturing system. The purpose of this manufacturing system is to build manufacturing clusters by grouping component families and machine cells with the aim of minimizing the total cost of production. In this paper, the cell formation problem is scrutinized by two similarity index methods. In the first phase, a new similarity coefficient method is proposed to identify the closeness of components/machines and this closeness is in the form of a similarity distance matrix. In the second phase, principal component analysis (PCA) and agglomerative clustering algorithm (ACA) are applied to group components into component families and machines into machine cells. In the third phase, a performance comparison of PCA and ACA was carried out with two different measures, viz. grouping efficiency and grouping efficacy. At the end, a complete factorial experiment is used to compare the results of the two algorithms, in which “Problem” is used as Factor A, “Algorithm” is used as Factor B and “Similarity Coefficient Method” is used as Factor C and the results are reported.